Showing papers on "Signal published in 2020"

Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling

Abstract: Intelligent reflecting surfaces can improve the communication between a source and a destination. The surface contains metamaterial that is configured to “reflect” the incident wave from the source towards the destination. Two incompatible pathloss models have been used in prior work. In this letter, we derive the far-field pathloss using physical optics techniques and explain why the surface consists of many elements that individually act as diffuse scatterers but can jointly beamform the signal in a desired direction with a certain beamwidth. We disprove one of the previously conjectured pathloss models.

Lightwave Technology: Telecommunication Systems

Abstract: Preface. 1. Introduction. 2. Optical Signal Generation. 3. Signal Propagation in Fibers. 4. Nonlinear Impairments. 5. Signal Recovery and Noise. 6. Optical Amplifier Noise. 7. Dispersion Management. 8. Nonlinearity Management. 9. WDM Systems. 10. Optical Networks. Appendix A: System of Units. Appendix B: Software Package. Appendix C: Acronyms. Index.

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

Abstract: We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity -modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on – off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each of the layers of the network Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.

Continuous Speech Separation: Dataset and Analysis

Abstract: This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior speech separation studies use pre-segmented audio signals, which are typically generated by mixing speech utterances on computers so that they fully overlap. Also, the separation algorithms have often been evaluated based on signal-based metrics such as signal-to-distortion ratio. However, in natural conversations, speech signals are continuous and contain both overlapped and overlap-free regions. In addition, the signal-based metrics only have weak correlation with automatic speech recognition (ASR) accuracy. Not only does this make it hard to assess the practical relevance of the tested algorithms, it also hinders researchers from developing systems that can be readily applied to real scenarios. In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a continuous audio stream that contains multiple utterances that are partially overlapped by a varying degree. A new real recording dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate conversations and capturing the audio replays with far-field microphones. A Kaldi-based ASR evaluation protocol is established by using a well-trained multi-conditional acoustic model. A recently proposed speaker-independent CSS algorithm is investigated by using LibriCSS. The dataset and evaluation scripts are made available to facilitate the research in this direction1.

Intelligent human computer interaction based on non redundant EMG signal

Abstract: Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

Micro triboelectric ultrasonic device for acoustic energy transfer and signal communication.

Abstract: As a promising energy converter, the requirement for miniaturization and high-accuracy of triboelectric nanogenerators always remains urgent. In this work, a micro triboelectric ultrasonic device was developed by integrating a triboelectric nanogenerator and micro-electro-mechanical systems technology. To date, it sets a world record for the smallest triboelectric device, with a 50 µm-sized diaphragm, and enables the working frequency to be brought to megahertz. This dramatically improves the miniaturization and chip integration of the triboelectric nanogenerator. With 63 kPa@1 MHz ultrasound input, the micro triboelectric ultrasonic device can generate the voltage signal of 16.8 mV and 12.7 mV through oil and sound-attenuation medium, respectively. It also achieved the signal-to-ratio of 20.54 dB and exhibited the practical potential for signal communication by modulating the incident ultrasound. Finally, detailed optimization approaches have also been proposed to further improve the output power of the micro triboelectric ultrasonic device. Miniaturizing efficient triboelectric nanogenerators remains a challenge. Here, the authors propose a MEMS-based micro triboelectric device for acoustic energy transfer and signal communication which is capable of generating the voltage signal of 16.8 mV and 12.7 mV through oil and sound-attenuation medium respectively with 63kPa at 1 MHz ultrasound input.

Multibranch and Multiscale CNN for Fault Diagnosis of Wheelset Bearings Under Strong Noise and Variable Load Condition

Abstract: The critical issue for fault diagnosis of wheel-set bearings in high-speed trains is to extract fault features from vibration signals. To handle high complexity, strong coupling, and low signal-to-noise ratio of the vibration signals, this article proposes a novel multibranch and multiscale convolutional neural network that can automatically learn and fuse abundant and complementary fault information from the multiple signal components and time scales of the vibration signals. The proposed method combines the conventional filtering methods and the idea of the multiscale learning, which can extend the breadth and depth of the feature learning process. Consequently, the proposed network can perform better. The experimental results on the wheelset bearing dataset demonstrate that the proposed method has better antinoise ability and load domain adaptability and can diagnose 12 fault types more accurately when compared with the five state-of-the-art networks.

Inflatable soft jumper inspired by shell snapping.

Abstract: Fluidic soft actuators are enlarging the robotics toolbox by providing flexible elements that can display highly complex deformations. Although these actuators are adaptable and inherently safe, their actuation speed is typically slow because the influx of fluid is limited by viscous forces. To overcome this limitation and realize soft actuators capable of rapid movements, we focused on spherical caps that exhibit isochoric snapping when pressurized under volume-controlled conditions. First, we noted that this snap-through instability leads to both a sudden release of energy and a fast cap displacement. Inspired by these findings, we investigated the response of actuators that comprise such spherical caps as building blocks and observed the same isochoric snapping mechanism upon inflation. Last, we demonstrated that this instability can be exploited to make these actuators jump even when inflated at a slow rate. Our study provides the foundation for the design of an emerging class of fluidic soft devices that can convert a slow input signal into a fast output deformation.

An Overview of Signal Processing Techniques for Terahertz Communications

Abstract: Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

UWB NLOS/LOS Classification Using Deep Learning Method

Abstract: Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the “clean” channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance.

A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery

Abstract: Time–frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong nonstationary characteristic or multicomponent signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this paper. It is shown that the energy concentration of the time–frequency representation (TFR) of a strong frequency-modulated signal from a PCT transform can be further enhanced by an SET transform. The technique can also be employed to accurately extract the signal components of a multicomponent nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR, which demonstrates the superiority of the current technique in dealing with nonstationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with nonstationary signals from practical sources.

Continuous and Scalable Manufacture of Hybridized Nano-Micro Triboelectric Yarns for Energy Harvesting and Signal Sensing.

Abstract: Textile-based triboelectric nanogenerators (TENG) that can effectively harvest biomechanical energy and sense multifunctional posture and movement have a wide range of applications in next-generation wearable and portable electronic devices. Hence, bulk production of fine yarns with high triboelectric output through a continuous manufacturing process is an urgent task. Here, an ultralight single-electrode triboelectric yarn (SETY) with helical hybridized nano-micro core-shell fiber bundles is fabricated by a facile and continuous electrospinning technology. The obtained SETY device exhibits ultralightness (0.33 mg cm-1), extra softness, and smaller size (350.66 μm in diameter) compared to those fabricated by conventional fabrication techniques. Based on such a textile-based TENG, high energy-harvesting performance (40.8 V, 0.705 μA cm-2, and 9.513 nC cm-2) was achieved by applying a 2.5 Hz mechanical drive of 5 N. Importantly, the triboelectric yarns can identify textile materials according to their different electron affinity energies. In addition, the triboelectric yarns are compatible with traditional textile technology and can be woven into a high-density plain fabric for harvesting biomechanical energy and are also competent for monitoring tiny signals from humans or insects.

Photonic RF Phase-Encoded Signal Generation With a Microcomb Source

Abstract: We demonstrate photonic RF phase encoding based on an integrated micro-comb source. By assembling single-cycle Gaussian pulse replicas using a transversal filtering structure, phase encoded waveforms can be generated by programming the weights of the wavelength channels. This approach eliminates the need for RF signal generators for RF carrier generation or arbitrary waveform generators for phase encoded signal generation. A large number of wavelengths—up to 60—were provided by the microcomb source, yielding a high pulse compression ratio of 30. Reconfigurable phase encoding rates ranging from 2 to 6 Gb/s were achieved by adjusting the length of each phase code. This article demonstrates the significant potentials of this microcomb-based approach to achieve high-speed RF photonic phase encoding with low cost and footprint.

Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band

Abstract: This study outlines the distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550 nm wavelength within supporting pumped wavelength of 1480 nm. The bias and modulation peak currents based distributed feedback laser are varied in order to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors. The signal power level, peak signal amplitude is measured against spectral wavelength and time bit period variations. The study emphasis the signal power level, peak signal amplitude are enhanced for the best selection values of both a bias current at 45 mA and modulation peak current at 0.5 mA.

Single-shot link discovery for terahertz wireless networks

Abstract: Of the many challenges in building a wireless network at terahertz frequencies, link discovery remains one of the most critical and least explored. In a network of mobile receivers using narrow directional beams, how do the nodes rapidly locate each other? This direction information is crucial for beam forming and steering, which are fundamental operations for maintaining link quality. As the carrier frequency increases into the terahertz range, the conventional methods used by existing networks become prohibitively time-consuming, so an alternative strategy is required. Using a leaky-wave antenna with a broadband transmitter, we demonstrate a single-shot approach for link discovery which can be accomplished much more rapidly. Our method relies on measurements of the width of a broad spectrum, and does not require any information about the phase of the received signal. This protocol, which relies on a detailed understanding of the radiation from leaky-wave devices, offers a realistic approach for enabling mobility in directional networks.

Speed Sensorless Control for Permanent Magnet Synchronous Motors Based on Finite Position Set

Abstract: This article presents a novel method for the sensorless control of interior permanent-magnet synchronous motors. An iterative search strategy based on dichotomy is proposed to provide a finite number of rotor position angles with good accuracy. These position angles are used to calculate the back electromotive force (EMF) in d -axis. The optimal rotor position angle is the one that yields a back EMF minimizing the defined cost function. With the increase of the iterations, the accuracy of rotor position angle increases geometrically. To effectively extract the back EMF signal under the low-speed condition, the high-frequency signal injection method is used to realize the low-speed operation of the motor. A hybrid control strategy is adopted to achieve the smooth switching from the low-speed to high-speed. The performance of the proposed method has been validated experimentally and compared with that of the conventional phase locked loop under different conditions.

Fast and accurate sCMOS noise correction for fluorescence microscopy.

Abstract: The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities. Scientific complementary metal-oxide semiconductor (sCMOS) cameras have advanced the imaging field, but they often suffer from additional noise compared to CCD sensors. Here the authors present a content-adaptive algorithm for the automatic correction of sCMOS-related noise for fluorescence microscopy.

Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary

Abstract: To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper. First, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Second, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.

Imaging nano-defects of metal waveguides using the microwave cavity interference enhancement method.

Abstract: Here, we demonstrate a microwave (MW) cavity interference enhancement method to image nano-defects on the surface of metal waveguide. The MW cavity interference system mainly consisted of a MW coaxial resonant cavity with a nano-probe. The MW signals have been evenly divided into two channels. One was the reference signal inputted into the MW waveguide and coupled into the MW cavity via the probe. Also, the coupling strength depends on the distance between the probe and the MW waveguide. Another one was directly inputted the MW cavity to interfere with the reference signal, and was enhanced in the cavity. Then, the surface topography of the metal waveguide was mapped by calculating the enhanced signals. In our experiment, a weak signal of ∼1 pW coupled from the waveguide can be detected by a MW cavity with the quality factor of ∼209. As a proof of application, the topography of nano-defects on the surface of metal waveguide in an MW chip has been mapped with a resolution of ∼15 nm. We have proved that this is a high-resolution, easy-to-manufacture, low-cost, and real-time online monitoring approach for online assessment and screening chips. This potentially has broad applications in the fields of chip manufacturing, chip inspection, nano-structure detection, and so on.

SpEx+: A Complete Time Domain Speaker Extraction Network.

Abstract: Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively.

Real-time precise point positioning with a low-cost dual-frequency GNSS device

Abstract: In recent years, there is an increasing demand for precise positioning with low-cost GNSS devices in support of applications from self-driving cars to unmanned aerial vehicles. Although single-frequency GNSS devices are still dominant in the low-cost market to date, some GNSS manufacturers have released low-cost dual-frequency GNSS devices, which are able to track new civilian signals such as L2C or L5. With dual-frequency GNSS measurements, the ionospheric delays can be eliminated by forming ionospheric-free combinations to further improve the positioning accuracy and reliability with low-cost GNSS devices. Extensive work has been conducted in the past for precise point positioning using high-end dual-frequency GNSS receivers. For low-cost GNSS-based PPP, there are some issues that need to be addressed. One is that current low-cost dual-frequency receivers can track only civil signals but not all GPS satellites currently transmit L2C or L5 civil signals. This means that fewer dual-frequency GNSS measurements are available for position determination. Another is that the measurement quality of low-cost receivers is not as good as high-end receivers. The above will not only increase the convergence time but also affect the obtainable positioning accuracy. We propose a new method in which not only the conventional dual-frequency ionospheric-free code and phase measurements, but also the single-frequency ionosphere-corrected code measurements with precise ionospheric products, are adopted for position determination. To be more specific, ionospheric-free code and phase combinations are applied to satellites with the second civil signal, while the single-frequency ionosphere-corrected code measurement is applied to all observed satellites. Both stationary and automotive experiments have been conducted to assess the performance of the new method. The field test results show that it can quickly reach half-meter accuracy in horizontal at a much faster convergence speed than the conventional DF-PPP which would usually take several minutes to reach a similar accuracy. This indicates that the new method is more suitable for mass-market applications with low-cost GNSS devices.

Generalized Kramers–Kronig receiver for coherent terahertz communications

Abstract: Modern communication systems rely on efficient quadrature amplitude modulation formats that encode information on both the amplitude and phase of an electromagnetic carrier. Coherent detection of such signals typically requires complex receivers that contain a continuous-wave local oscillator as a phase reference and a mixer circuit for spectral down-conversion. In optical communications, the so-called Kramers–Kronig scheme has been demonstrated to simplify the receiver, reducing the hardware to a single photodiode1–3. In this approach, a local-oscillator tone is transmitted along with the signal, and the amplitude and phase of the complex signal envelope are digitally reconstructed from the photocurrent by exploiting their Kramers–Kronig-type relation4–6. Here, we transfer the Kramers–Kronig scheme to high-speed wireless communications at terahertz carrier frequencies. To this end, we generalize the approach to account for non-quadratic receiver characteristics and employ a Schottky-barrier diode as a nonlinear receiver element. Using 16-state quadrature amplitude modulation, we transmit a net data rate of 115 Gbit s−1 at a carrier frequency of 0.3 THz over a distance of 110 m. The Kramers–Kronig approach is applied to high-capacity, free-space terahertz communications, bringing a greatly simplified receiver design.

The research on 220GHz multicarrier high-speed communication system

Abstract: This paper presents our investigation into a 220 GHz multicarrier high-speed communication system based on solid state transceivers. The proposed system has eased the demand of high sampling rate analog-to-digital converter (ADC) by providing several signal carriers in microwave band and converting them to 220 GHz channel. The system consists of a set of 220 GHz solid-state transceiver with 2 signal carriers, two base-bands for 4 GSPS ADCs. It has achieved 12.8 Gbps rate real-time signal transmission using 16QAM modulation over a distance of 20 m without any other auxiliary equipment or test instruments. The baseband algorithm overcomes the problem of frequency difference generates by non-coherent structure, which guarantees the feasibility of long-distance transmission application. Most importantly, the proposed system has already carried out multi-channel 8K video parallel transmission through switch equipment, which shows the multicarrier high-speed communication system in submillimeter wave has great application prospects. To the best of the authors' knowledge, this is the first all-solid-state electronics multicarrier communication system in submillimeter and terahertz band.

Signal and System Design for Wireless Power Transfer: Prototype, Experiment and Validation

Abstract: A new line of research on communications and signals design for Wireless Power Transfer (WPT) has recently emerged in the communication literature. Promising signal strategies to maximize the power transfer efficiency of WPT rely on (energy) beamforming, waveform, modulation and transmit diversity, and a combination thereof. To a great extent, the study of those strategies has so far been limited to theoretical performance analysis. In this paper, we study the real over-the-air performance of all the aforementioned signal strategies for WPT. To that end, we have designed, prototyped and experimented an innovative radiative WPT architecture based on Software-Defined Radio (SDR) that can operate in open-loop and closed-loop (with channel acquisition at the transmitter) modes. The prototype consists of three important blocks, namely the channel estimator, the signal generator, and the energy harvester. The experiments have been conducted in a variety of deployments, including frequency flat and frequency selective channels, under static and mobility conditions. Experiments highlight that a channel-adaptive WPT architecture based on joint beamforming and waveform design offers significant performance improvements in harvested DC power over conventional single-antenna/multi-antenna continuous wave systems. The experimental results fully validate the observations predicted from the theoretical signal designs and confirm the crucial and beneficial role played by the energy harvester nonlinearity.

Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals

Abstract: The neurological disease such as the epilepsy is diagnosed using the analysis of electroencephalogram (EEG) recordings. The areas of the brain associated with the consequence of epilepsy are termed as epileptogenic regions. The focal EEG signals are generated from epileptogenic areas, and the nonfocal signals are obtained from other regions of the brain. Thus, the classification of the focal and non-focal EEG signals are necessary for locating the epileptogenic areas during surgery for epilepsy. In this paper, we propose a novel method for the automated classification of focal and non-focal EEG signals. The method is based on the use of the synchrosqueezing transform (SST) and deep convolutional neural network (CNN) for the classification. The time-frequency matrices of EEG signal are evaluated using both Fourier SST (FSST) and wavelet SST (WSST). The two-dimensional (2D) deep CNN is used for the classification using the time-frequency matrix of EEG signals. The experimental results reveal that the proposed method attains the accuracy, sensitivity, and specificity values of more than 99% for the classification of focal and non-focal EEG signals. The method is compared with existing approaches for the discrimination of focal and non-focal categories of EEG signals.